EP2908913B1 - Signatures moléculaires du cancer de l'ovaire - Google Patents

Signatures moléculaires du cancer de l'ovaire Download PDF

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EP2908913B1
EP2908913B1 EP13848075.1A EP13848075A EP2908913B1 EP 2908913 B1 EP2908913 B1 EP 2908913B1 EP 13848075 A EP13848075 A EP 13848075A EP 2908913 B1 EP2908913 B1 EP 2908913B1
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cancer
cells
signature
genes
ovarian cancer
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EP2908913A4 (fr
EP2908913A1 (fr
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Sandra ORSULIC
Beth Y. KARLAN
Xiaojian CUI
Mourad TIGHIOUART
Dong-Joo CHEON
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Cedars Sinai Medical Center
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    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
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    • G01N33/57449—Specifically defined cancers of ovaries
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/16—Primer sets for multiplex assays

Definitions

  • This invention relates to genetic pathways involved in ovarian cancer growth; including, molecular signatures associated with identifying molecular subtypes of gynecological diseases and/or conditions, such as ovarian cancer.
  • molecular signatures have wide application for classification of patent populations, prognosis, diagnosis and treatment of gynecological diseases and/or conditions, such as ovarian cancer.
  • Ovarian cancer is the leading cause of gynecologic cancer deaths in the United States. Despite similarities in initial disease presentation, the existence of molecular subtypes of ovarian cancer is suggested by clinical outcomes displaying a broad range of survival end points. For example, some patients develop a chronic-type disease that can be maintained on chemotherapy for more than five years. Others are intrinsically resistant to chemotherapy or initially respond to chemotherapy, but then rapidly become resistant to the treatment and subsequently have low response rates to other second-line agents. Strategic approaches customized for treating these different patient groups is lacking, as ovarian cancer therapy is implemented on a watch-and-wait basis.
  • CSCs cancer stem cells
  • WO2004/022778 relates to methods of diagnosis and prognosis of ovarian cancer using sets of specific gene panels.
  • Article entitled " Multi-cancer computational analysis reveals invasion-associated variant of desmoplastic reaction involving INHBA, THBS2 and COL11A1" by Hoon Kim et. at. relates to identifying data from multiple cancers using a novel computational method identifying sets of genes whose coordinated over expression indicates the presence of a particular phenotype in high stage cancer.
  • Described herein are gene signatures providing prognostic, diagnostic, treatment and molecular subtype classifications of ovarian cancers. Biostatistical methods are applied across a variety of studies encompassing a wide array of laboratory and clinical variables, thereby leading to generation of ovarian cancer disease signatures (OCDSs) that account for molecular heterogeneity present in gynecological cancers. Statistical analysis across multiple independent data sets allows generation of a preliminary ovarian cancer fixed signature (OCFS), a comprehensive definition of the core programming of disease development. Also described herein is a specific biochemical definition of an ovarian cancer stem cell identified via an (OCSC) signature.
  • OCFS preliminary ovarian cancer fixed signature
  • a specific biochemical definition of an ovarian cancer stem cell identified via an (OCSC) signature are also described herein.
  • ovarian cancer cell lines including ovarian cancer stem cell lines (OCSC), and selectively labeled animal models provides in vitro and in vivo models for applying the aforementioned signatures to develop clinical applications, such personalized treatment strategies focused on molecular subtypes of gynecological cancers.
  • OCSC ovarian cancer stem cell lines
  • Described herein is a method of determining a prognosis of cancer in an individual, including determining the presence or absence of a high level of expression in the individual relative to a normal baseline standard for a single prognostic panel of the following markers, ACTA2, ADAM12, AEBP1, COL11A1, COL3A1, COL5A1, COL6A2, CYR61, DCN, FN1, GREM1, LOX, LUM, POSTN, SNAI2, SPARC, TAGLN, THBS2, TIMP3, VCAN, and/or VIM, and prognosing a case of cancer if the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of at least one of the markers.
  • markers ACTA2, ADAM12, AEBP1, COL11A1, COL3A1, COL5A1, COL6A2, CYR61, DCN, FN1, GREM1, LOX, LUM, POSTN, SNAI2, SPARC,
  • the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of at least two, three, four, or five of the markers. In other embodiments, the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of at least six, seven, eight, nine, ten or more of the markers.
  • the cancer is ovarian cancer.
  • the prognosis provides a therapeutic selection for the prognosed individual, selected from the group consisting of: chemotherapy, radiotherapy, surgery, and combinations thereof.
  • the markers are AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, and VCAN
  • Also described herein is a method of determining a diagnosis of cancer in an individual suspected of having cancer, including: obtaining sample from an individual suspected of having cancer; determining the presence or absence of a high level of expression in the individual relative to a normal baseline standard for a single diagnostic panel including the following markers ACTA2, ADAM12, AEBP1, COL11A1, COL3A1, COL5A1, COL6A2, CYR61, DCN, FN1, GREM1, LOX, LUM, POSTN, SNAI2, SPARC, TAGLN, THBS2, TIMP3, VCAN, and/or VIM, and diagnosing a case of cancer if the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of at least one of the markers.
  • the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of at least two, three, four, or five of the markers. In other embodiments, the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of at least six, seven, eight, nine, ten or more of the markers.
  • the cancer is ovarian cancer.
  • the diagnosis provides a molecular subtype classification for the diagnosed case of cancer in the individual.
  • the markers are AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, and VCAN
  • a method of modulating a tumor phenotype in an individual including providing a quantity of an agent capable of modulating cancer stem cell (CSC) function, and administering the quantity of the agent to an individual, wherein modulation of CSC function results in modulation of a tumor phenotype in the individual.
  • the individual has cancer.
  • the cancer is ovarian cancer.
  • the agent is a small molecule, nucleic acid, anti-sense oligonucleotide, aptamer, protein, peptide and/or antibody.
  • the protein is collagenase.
  • the antibody is specific for CD24, CD44, CD117, CD133 or ALDH1. In other embodiments, the antibody modulates TGF- ⁇ pathway activity.
  • compositions including an isolated population of cancer stem cells (CSCs) obtained from an individual afflicted with cancer.
  • the cancer is ovarian cancer.
  • the composition is a cultured cell line.
  • the isolated cells include cancer stem cells (CSCs) and non-CSCs
  • adding a detectable reagent that preferentially binds to CSCs to the isolated cells measuring a quantity of detectable reagent bound to the isolated cells and applying a ratio to the quantity, wherein application of the ratio to the quantity indicates the proportion of the isolated cells that are CSCs.
  • the detectable reagent is an antibody specific for CD24, CD44, CD117, CD133 or ALDH1.
  • the quantity of detectable reagent comprises flow cytometry, immunohistochemistry, immunocytochemistry, or enzyme-linked immunoassay (ELISA).
  • an assay for determining the subtype of a gynecological cancer including determining the presence or absence of a high level of expression in the individual relative to a normal baseline standard for a single prognostic panel comprising the following markers, ACTA2, ADAM12, AEBP1, COL11A1, COL3A1, COL5A1, COL6A2, CYR61, DCN, FN1, GREM1, LOX, LUM, POSTN, SNAI2, SPARC, TAGLN, THBS2, TIMP3, VCAN, and/or VIM, and determining the subtype of the gynecological cancer a case of cancer if the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of at least one of the markers.
  • the gynecological cancer is ovarian cancer.
  • the ovarian cancer is characterized by elevated stromal or epithenlial-to-mesechymal transition activity.
  • the markers are AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, and VCAN.
  • determining the subtype of the gynecological cancer indicates a therapeutic treatment.
  • therapeutic treatment is immunotherapy.
  • the therapeutic treatment is anti-fibrotic.
  • the therapeutic treatment modulates TGF- ⁇ pathway activity.
  • ovarian cancer patients are diagnosed at a late stage when the cancer has metastasized throughout the peritoneal cavity.
  • Standard clinical management involves surgical tumor debulking followed by administration of platinum- and taxane-based chemotherapy.
  • Patients with advanced ovarian cancer display a broad range of survival end points, despite the similarities in initial disease presentation (e.g., late-stage), histopathology (e.g., high-grade, serous ovarian cancer) and treatment (e.g., surgery followed by platinum- and taxane-based chemotherapy).
  • initial disease presentation e.g., late-stage
  • histopathology e.g., high-grade, serous ovarian cancer
  • treatment e.g., surgery followed by platinum- and taxane-based chemotherapy.
  • some patients develop a chronic-type disease and can live with ovarian cancer (despite intermittent chemotherapy treatments) for 5, 10 or more years.
  • Others have tumors that are intrinsically resistant to chemotherapy or initially respond to chemotherapy but then rapidly recur due to regrowth of resistant disease and
  • CSCs cancer stem cells
  • stroma stroma cells
  • ovarian cancer stem cell ovarian cancer stem cell
  • OCSC ovarian cancer stem cell
  • the existence and role of CSCs in the ovarian cancer context is not well-understood.
  • tumors display morphological, phenotypical, and biochemical heterogeneity, different cells may possess only some or many hallmarks of a posited OCSC, yet fail to be a bona fide OCSC.
  • Application of the described techniques can lead to positive identification of OCSCs and compilation of a precise, biochemical definition via an OCSC signature. Generation of such a signature contributes further in diagnostic and prognostic techniques would further benefit from early stage detection of OCSCs, as these cells are understood to be key actors in neoplastic tumor formation.
  • solid tumors are now understood to be complex "organs” comprised of not only malignant cells, such as OCSCs, but also non-malignant cells, described under the umbrella term, "stroma".
  • malignant cells such as OCSCs
  • stroma non-malignant cells
  • Most outcome-predicting gene signatures originate in cancer cells.
  • stromal cells are active contributors to tumor progression and that large amounts of tumor stroma are also associated with poor clinical outcome in multiple solid tumors.
  • This dynamic bi-directional interaction between the malignant cells and "stroma” is not well understood, but recent studies have suggested “stroma” can modify the neoplastic properties of tumor cells and that tumor cells re-program the "stroma” to generate a unique microenvironment that is crucial for cancer survival, homeostasis, progression, and metastasis.
  • Described herein is the development of gene signatures providing prognostic, diagnostic, treatment methods and molecular subtype classifications of ovarian cancers.
  • Biostatistical methods are applied to link molecular, laboratory, and clinical data obtained from a variety of studies encompassing a wide array of laboratory and clinical variables.
  • Each of these studies provides ovarian cancer disease signatures (OCDSs) that account for molecular heterogeneity present in gynecological cancers.
  • OCFS ovarian cancer disease signature
  • robust statistical analysis across these multiple independent data sets allows further optimization for generating an ovarian cancer fixed signature (OCFS).
  • OCFS ovarian cancer fixed signature
  • generation of an OCFS helps to define the core programming of disease development.
  • ovarian cancer stem cell via an (OCSC) signature Described further herein is a specific biochemical definition of an ovarian cancer stem cell via an (OCSC) signature.
  • OCSC ovarian cancer stem cell lines
  • selectively labeled animal models provides in vitro and in vivo models for developing clinical applications, such as guided personalized treatment decisions.
  • the various identified gene signatures such as OCFS is correlated with ovarian cancer progression and poor outcome.
  • the OCFS signature has strong predictive value, biological relevance, and translational potential. Future studies are warranted to optimize the gene signature for its predictive power and develop a quantitative assay that is appropriate for use in the clinical setting.
  • Using the validated gene signature to identify patients who are unlikely to respond to standard treatment will provide opportunities to deliver individualized therapies that target the underlying mechanism of the poor outcome signature genes.
  • a better understanding of how collagen remodeling contributes to ovarian cancer progression and metastasis could reveal the "Achilles heel" of these tumors and thus have a major impact on the development of improved therapies for advanced ovarian cancer.
  • the present invention includes a method of determining a prognosis of cancer in an individual includes, determining the presence or absence of a high level of expression in the individual relative to a normal baseline standard for a prognostic panel including the following markers:AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3 and VCAN and prognosing a case of cancer if the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of the markers in the prognostic panel.
  • the cancer is ovarian cancer.
  • the prognosis provides a therapeutic selection for the prognosed individual, selected from the group consisting of: chemotherapy, radiotherapy, surgery, and/or combinations thereof.
  • the markers are associated with the presence of ovarian cancer stem cells (OCSCs). In other embodiments, the markers are associated with poor survival. In other embodiments, the markers are associated with poor prognosis. In other embodiments, the markers are associated with late-stage ovarian cancer, high-grade, or serious ovarian cancer.
  • OCSCs ovarian cancer stem cells
  • the prognostic panel includes the markers listed in Table 1.
  • Table 1. Representative Ovarian Cancer Stem Cell (OCSC) Genes ABCD2 CNN1 FAM55C IL7R OGN SPARC YWHAB ACTA2 COL11A1 FGF1 KATNA1 OLFML3 SPOCK2 ZFHX4 ACTG2 COL1A2 FGFR2 KSR1 PCMT1 SPP1 ZNF804A ADAM 12 COL3A1 FLNC LAMA4 PDGFRA SV2A ALDH1A1 COL6A1 FN1 LAMB1 PHLDB2 TAGLN ALDH1A2 COLEC12 FNTA LOX PI3 TFPI2 AMACR CTGF FXYD5 LOXL1 PIEZO2 TGFB2 ANGPT1 CYBRD1 GADD45B LRRC17 PPIC TGFBI ANGPTL2 CYR61 GAS1 LUM PPP3CA THBS1 ANGPTL4 DCLK1
  • the single prognostic panel includes the markers listed in Table 2.
  • Table 2. Representative Periostin (POSTN)-Coexpressing Genes ACTA2 CNN1 EPAS1 IL6 MMP11 RAI14 THBD ACTC1 COL10A1 EPYC IL7R MMP19 RASAL3 THBS1 ACTG2 COL11A1 ETV1 INHBA MMP1 RASSF2 THBS2 ACTR2 COL12A1 FAM26E ITGA1 MOXD1 RCAN1 TIMP3 ADAM 12 COL1A2 FAP ITGA5 MPP1 RGS16 TMEM89 AEBP1 COL1A1 FBLN1 ITGA11 MS4A4A SCHIP1 TMEM158 AK5 COL3A1 FBLN2 ITGB1 MXRA5 SDC1 TMEM217 ALDH1A3 COL4A2 FBN1 ITGBL 1 NCF2 SEC13 TMEM45A ANGPTL2 COL5A1
  • the single prognostic panel includes the markers listed in Table 3.
  • Table 3. Representative Genes Associated with Poor Survival
  • ADAM 12 CILP CYR61 FBLN2 INHBA PDLIM3 SNAI2 ADH1B COL10A1 DIO2 FGF1 ITGBL1 PDPN SPON2 ADIPOQ COL11A1 DPT FMO1 LOX PIEZO2 SULF1 AEBP1 COL1A1 DUSP1 FOSB LPPR4 PLAU TDO2 ASPN COL5A1 DUSP5 GLT8D2 MATN3 POSTN THBS2 ATF3 COL5A2 ECM1 GREM1 MFAP5 PPBP TIMP3 C1QTNF3 COL5A3 EDNRA GUCY1A3 MMP11 PRKG1 VCAM1 C7orf10 COL6A2 EGR1 HAS2 MN1 PRRX1 VCAN CALB2 COMP EGR2 HBA1/HBA2 NR4A1 PTG
  • the single prognostic panel includes the markers listed in Table 4 or 4a.
  • Table 4. - Representative Genes Associated with Poor Prognosis ABCA8 COL6A3 FOSB LMOD1 POSTN TIMP3 ACTA2 CXCL12 HBA1/HBA2 LOX PTGIS TMEM47 ADH1B CYP1B1 HBB LUM RARRES1 TMEM158 AEBP1 DCN HEPH MAL RGS2 TUBB2A ALDH1A1 DUSP1 ID1 NBL1 RHOB UBD ALDH1A2 EDNRA IGF2 NR2F2 SEMA3C VCAN CAV1 EFEMP 1 IGFBP3 NR4A2 SERPINE1 VIM CDH11 EGR1 IGFBP4 OLFML3 SERPINF1 ZEB1 COL11A1 FBLN1 IGFBP5 OSR2 SNAI2 COL3A1 FBN1 ISLR PDGFRA SPARC COL5A1 FN1 ITM
  • the method of determining a prognosis of cancer in an individual includes, determining the presence or absence of a high level of expression in the individual relative to a normal baseline standard for a prognostic panel including the markers listed in Table 4a.
  • the cancer is ovarian cancer.
  • the prognosis provides a therapeutic selection for the prognosed individual, selected from the group consisting of: chemotherapy, radiotherapy, surgery, and/or combinations thereof. The markers are associated with poor prognosis.
  • described herein is a method of determining a diagnosis of cancer in an individual suspected of having cancer, including obtaining sample from an individual suspected of having cancer, determining the presence or absence of a high level of expression in the individual relative to a normal baseline standard for a single diagnostic panel, and diagnosing a case of cancer if the individual demonstrates the presence of a high level of expression relative to a normal baseline standard of the markers listed in Table 4a.
  • the isolated cells include cancer stem cells (CSCs) and non-CSCs
  • adding a detectable reagent that preferentially binds to CSCs to the isolated cells measuring a quantity of detectable reagent bound to the isolated cells, applying a ratio to the quantity, wherein application of the ratio to the quantity indicates the proportion of the isolated cells that are CSCs.
  • the detectable reagent is an antibody specific for CD24, CD44, CD117, CD133 or ALDH1.
  • the quantity of detectable reagent comprises flow cytometry, immunohistochemistry, immunocytochemistry, or enzyme-linked immunoassay (ELISA).
  • compositions including an enriched population of cancer stem cells (CSCs) obtained from an individual afflicted with cancer, wherein the CSCs express a higher level of at least one CSC marker when compared to non-CSCs, and wherein the CSCs are capable of self-renewal and differentiation.
  • the least one CSC marker includes one or more markers listed in Table 1.
  • the cancer is a gynecological cancer.
  • the cancer is ovarian cancer.
  • the composition is a cultured cell line.
  • described herein is a composition including an isolated population of cancer stem cells (CSCs) obtained from an individual afflicted with cancer.
  • the cancer is ovarian cancer.
  • the composition is a cultured cell line.
  • a method of modulating a tumor phenotype in an individual including providing a quantity of an agent capable of modulating cancer stem cell (CSC) function, and administering the quantity of the agent to an individual, wherein modulation of CSC function results in modulation of a tumor phenotype in the individual.
  • the individual has cancer.
  • the cancer is ovarian cancer.
  • the agent is a small molecule, nucleic acid, protein, peptide and/or antibody.
  • the protein is collagenase.
  • the small molecule is Salinomycin, Etoposide, Abamectin, Nigericin, Resveratrol, MS-275, Ciclopirox, Quinostatin, Alsterpaullone, Azacitidine, Bepridil, Fluspirilene, Cortisone, Etoposide, Loperamide, Ikarugamycin, Pyrvinium, Irinotecan, Phenoxybenzamine, Solanine Nicergoline, Monobenzone, Ellipticine, Norcyclobenzaprine, Tobramycin, Gossypol, Ethambutol, Daunorubicin, Methotrexate, Dextromethorphan, Thiostrepton, Propylthiouracil, Clotrimazole, Amiodarone, Thioguanosine, Rimexolone, Tranylcypromine, Ginkgolide A, GW-8510, Hycanthone, Rolitetracycline, Dipyridamole, Perphenazine, Beta-
  • the nucleic acid is a small interefering RNA (siRNA) or short hairpin RNA (shRNA).
  • siRNA or shRNA is cognate to fibroblast growth factor 1 (FGF1), fibronectin (FN1), or L1CAM.
  • FGF1 fibroblast growth factor 1
  • FN1 fibronectin
  • L1CAM fibroblast growth factor 1
  • the antibody is specific for ABCC5, CD24, CD44, CD117, CD133 or ALDH1.
  • the agent is capable of modulating the rate of epithelial-to-mesenchymal transition (EMT).
  • modulating a tumor phenotype includes treating an individual afflicted with cancer.
  • a method of modulating a tumor phenotype in an individual including providing a quantity of an agent capable of modulating TGF-beta pathways, and administering the quantity of the agent to an individual, wherein modulation of TGF-beta pathways results in modulation of a tumor phenotype in the individual.
  • the individual has cancer.
  • the cancer is ovarian cancer.
  • the agent is a small molecule, nucleic acid, protein, peptide and/or antibody.
  • the agent is a ligand traps, antisense oligonucleotide (ASO), small molecule receptor kinase inhibitor, or peptide aptamer.
  • ligand traps can also include anti-ligand neutralizing antibodies and soluble decoy receptor proteins incorporating the ectodomains from either T ⁇ RII or ⁇ RIII/betaglycan protein, such as TGF- ⁇ monoclonal antibody, 1D11, or decoy receptor proteins such as recombinant Fc-fusion proteins with the soluble ectodomain of either T ⁇ RII (T ⁇ RII-Fc) or the type III receptor, betaglycan.
  • ASOs include nucleotides capable of reducing the bioavailability of active TGF- ⁇ ligands such as AP12009 (Trabedersen).
  • small molecule receptor kinase inhibitors include small molecule inhibitor of T ⁇ RI, SB-431542, T ⁇ RI/ALK5 kinase inhibitor, Ki26894, T ⁇ RI inhibitor SD-208, dual inhibitor of T ⁇ RI/II, LY2109761, or inhibitors selective for the kinase domain of the type 1 TGF- ⁇ receptor, LY2157299.
  • other therapeutics targeting related pathways such as EGFR (erlotinib), ABL/PDGFR/KIT (imatinib), and VEGFR/RAF/PDGFR (sorafenib), may be used in combination with a TGF-beta related therapeutic.
  • the agent targetings intracellular TGF- ⁇ signaling molecules such as Smads.
  • aptamers are small peptide molecules containing a target-binding and a scaffolding domain that stabilizes and interferes with the function of the targets, and can be designed specifically against Smas such as Smad2 or Smad3, the Trx-SARA aptamer is one such example.
  • the nucleic acid is a small interefering RNA (siRNA) or short hairpin RNA (shRNA).
  • the siRNA or shRNA is cognate to ACTA2, ADAM12, COL11A1, COL3A1, COL5A1, COL6A2, CYR61, DCN, FN1, GREM1, LOX, POSTN, SNAI2, SPARC, TAGLN, TIMP3, VCAN or VIM.
  • the siRNA or shRNA is cognate to AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, or VCAN.
  • the siRNA or shRNA is cognate to COL11A1, LOX, POSTN, THBS2, or VCAN.
  • a method including providing isolated cells obtained from an individual afflicted with cancer, wherein the isolated cells include cancer stem cells (CSCs) and non-CSCs, adding a detectable reagent that preferentially binds to CSCs to the isolated cells, measuring a quantity of detectable reagent bound to the isolated cells, applying a ratio to the quantity, wherein application of the ratio to the quantity indicates the proportion of the isolated cells that are CSCs.
  • the detectable reagent is an antibody specific for ABCC5, CD24, CD44, CD117, CD133 or ALDH1.
  • the quantity of detectable reagent comprises flow cytometry, immunohistochemistry, immunocytochemistry, or enzyme-linked immunoassay (ELISA).
  • Ovarian cancer stem cells generally include
  • CSCs cancer stem cells
  • OCSC bona fide ovarian cancer stem cells
  • tumors display morphological, phenotypical, and biochemical heterogeneity, it is understood that different cells may possess only some or many hallmarks of a posited OCSC, yet fail to be a bona fide OCSC.
  • Several leading OCSC candidates include, for example, cells expressing markers CD24, CD44, CD117, CD133 and ALDH1.
  • CD133/ALDH1 double markers have been shown most reliable to enrich for OCSC candidates, as further described via morphologhical and functional characteristics, such as spheroid formation, cisplatin resistance, clinical outcome, and perhaps most importantly, tumor formation with as few as 30 cells (i.e., high tumor seeding potential).
  • OCSC candidates may display some or many characteristics of a bona fide OCSC, there is yet no positive determination of which particular cells drawn from heterogeneous populations within tumors display all of the "stemness" characteristics of a bona fide OCSC.
  • Endpoint studies demonstrating capture of bona fide OCSC could be shown experimentally by isolation of a cell capable of recapitulating the generation of a continuously growing tumor.
  • serial transplantation in animal models provides a functional assay for the two CSC hallmarks, self-renewal and differentiation.
  • these endpoint functional studies fail to provide a molecular snapshot of the biochemical actors responsible for giving CSCs, including OCSCs, their unique properties. Therefore, an OCSC signature provides two important uses. The first is a specific, biochemical definition of a bona fide OCSC, which currently does not exist. The second includes applications for prognostic and diagnostic use, as a type of ovarian cancer disease signature (OCDS).
  • OCDS ovarian cancer disease signature
  • OCSC ovarian cancer stem cell
  • an OCSC provides a precise biochemical definition of a specific cell type for identification
  • application of an OCSC in a prognostic or diagnostic context appears to find further utility as capable of classifying molecular subtypes of cancer.
  • ovarian cancer like other cancers, is a multi-etiological disease, and variations in tumor cell origin, tissue compartment development, and/or other factors leads to the manifestation of disease subtypes.
  • Tumor samples may include higher or lower numbers of OCSCs, as demonstrated by detection of an OCSC signature, and this variation may prove to be highly informative of clinical outcomes (e.g., chemoresistance, survival).
  • an OCSC may be considered as a type of OCDS, as capable of prognostic and diagnostic applications.
  • OCSC signature In order to identify an OCSC core transcriptional program (OCSC signature), the inventors isolated OCSC candidates and non-CSCs using ALDH1 and CD133/ALDH1 from A2780 human ovarian cancer cell line. As OCSCs are a rare and evanescent cell population, the A2780 cell line provides the greatest number OCSC candidates (1%) for analysis. The inventors then analyzed the CD133+ALDH1+ OCSC candidates using Affymetrix Human Gene Array, normalized the data with two independent algorithms, and compared their expression profiling to each other as well as to public array databases (OncoMine, Stanford Microarray database, Gene Expression Omnibus) ( Fig.1 ). List of OCSC genes constituting a preliminary signature are shown in Table 1.
  • Fig.2A Molecular phenotyping of human ovarian cancer stem cells unravels the mechanisms for repair and chemoresistance. Cell Cycle. 8(1):158-66 .
  • Baba et al. (2009) Epigenetic regulation of CD133 and tumorigenicity of CD133+ ovarian cancer cells. Oncogene. 28(2):209-18 .
  • OCSC candidates e.g., CD133+ALDH1+ or CD44+ and CD133+
  • these "stemness” genes could be used to identify bona fide OCSCs.
  • core transcriptional programming as tied to "stemness” characteristics relate to genes involved in pluripotency (e.g., transcription factors), self-renewal and differentiation (e.g., growth factors, epithelial-mesenchymal transition), surface antigens (e.g., adhesion markers, migration factors, matrix production), and metabolic regulators.
  • the preliminary identification of an OCSC signature predicted candidate genes for potential therapeutic intervention and poor patient survival in public databases. Further validation of our preliminary OCSC signature using patient samples, in vitro cell culture, and statistical analysis are applied to determine prognostic and therapeutic power of an OCSC signature.
  • establishment of a comprehensive, global OCSC signature provides a series of biomarkers, wherein expression levels of one, some or all of the genetic markers, as a transcript or protein level, may be used to prognose a range of clinical outcomes for a patient, such as chemoresistance or survival.
  • an OCSC signature provides a definitive biochemical approach to positively identifying the proportion of cells in a tumor sample that can be confirmed as bona fide OCSCs. This focus on the percentage of cell populations within a tumor samples, is critical as higher numbers are CSCs are generally understood to be indicators for poor prognostic outcomes (e.g., chemoresistance, increased tumor-sphere-forming ability, neoplastic regrowth).
  • the genetic markers may serve as useful diagnostic tools to provide molecular subtype classifications of a gynecological cancer, such as ovarian cancer. This is of particular importance in the context of cancer, given the multi-etiological nature of these diseases.
  • a comprehensive OCSC signature allows targeted therapeutic intervention focused on OCSC eradication and/or retardation.
  • current chemotherapeutic agents target the bulk population of cancer cells, and tumor regression may be observed.
  • reducing bulk cancer cells that play a smaller role in tumor recurrence and chemoresistance may explain why tumor regression does not necessarily translate into increased patient survival.
  • reduction of bulk cancer cell populations has been shown in certain contexts to enrich CSC populations, thereby positively the most noxious cells, OCSCs, that are responsible for poor clinical outcomes.
  • therapeutic approaches rely on chemical compounds (e.g., small molecule inhibitors) that selectively target OCSCs, thereby retarding CSC viability, spheroid formation, and/or limiting chemoresistance.
  • OCSC candidates As described, a variety of OCSC candidates have been described, which may possess some or all of the features of bona fide OCSCs.
  • ovarian cancer like other cancers, is a multi-etiological disease, variations in tumor cell origin, tissue compartment development, and/or other factors leads to the manifestation of disease subtypes, these subtypes exhibiting variations in clinical outcomes (e.g., chemoresistance, survival). Therefore, one may establish a preliminary OCSC signature, as obtained from cell lines, or clinical samples. Such preliminary OCSC signatures require further validation across a wider and more diverse array of cell lines and samples. This establishes those features consistently found across OCSC signatures from variable sources, thereby leading to establishment of a comprehensive, global OCSC signature.
  • This comprehensive, global OCSC signature minimizes variation attributable to the cell source, thereby providing a biochemical definition of core transcriptional machinery responsible for providing the "stemness" characteristics of OCSCs, and one of that is agnostic to the originating source material.
  • OCSC global OCSC signature
  • isolating OCSC candidates from surgical specimens and other ovarian cancer cell lines (Table 6). Further comparison with existing studies on OCSC candidates can also prove to be informative. An example of a meta-analysis conducted across existing studies is shown (Table 7).
  • RNA can be extracted, and expression of OCSC biomarkers can be measured by Affymetrix Human Gene Array or quantitative real-time PCR. Non-CSCs isolated from each sample serve as controls to identify those genes uniquely upregulated in OCSCs. Table 6.
  • OCSC Markers to be Used to Isolate OCSC Candidates CSC marker Sample Expected CSC % CD133+*ALDH1+ Solid tumors, ascites 0.1 % CD133+ Solid tumors, ascites 1-20 % CD133+ OVCAR8 cell line** 40 % *ALDH1+ OVCAR8 cell line 1.9 % *ALDH1+ HEY1 cell line** 6.5 % * ALDH1 activity is determined by ALDEFLUOR kit (Stem cell technologies, Inc). **CD133+ALDH1+ population exists under ⁇ 0.03% in these cell lines. Table 7. Meta-Analysis of Cancer Stem Cell Microarray Data Comparison CSC v.
  • establishing the predictive power of an OCSC signature can be determined by utilizing high-throughput qRT-PCR of the validated OCSC biomarkers in patient samples.
  • One example includes the ABI Open Array® Real-time PCR system, wherein custom-designed array plates containing validated TaqMan ® Real-time probes for 50 ovarian CSC identified biomarkers in duplicate are applied to patient samples.
  • Various endogenous controls ACTB, GAPDH, 18s rRNA, GUSB, PPIA, TBP, RPLP0, RPL4 are used for each plate for the normalization of all plates and samples.
  • OCSC biomarkers Some examples of validation of preliminary OCSC biomarkers are shown in Fig. 4 .
  • Enhanced expression of OCSC biomarkers is confirmed, as drawn from CD133+ALDH+ ovarian cancer stem cells (OCSCs) candidates, and CD133-ALDH- non-OCSC cells. It is clearly observed that OCSC biomarkers are highly expressed in OCSC when compared to non-OCSC cells. This includes the example OCSC signature "hub" genes, aldehyde dehydrogenase 1 family member a2 (ALDH1A2), fibroblast growth factor 1 (FGF1), and thrombospondin (THBS1).
  • ALDH1A2 aldehyde dehydrogenase 1 family member a2
  • FGF1 fibroblast growth factor 1
  • THBS1 thrombospondin
  • the expression data obtained from the qRT-PCR arrays are then matched to each patient clinical data.
  • clinical features for analysis include FIGO stage, tumor grade, chemotherapy resistance, and survival.
  • OCSC biomarkers such as ALDH1A2, ANGPTL4, COL1A2, COL3A1, COL6A1, EFEMP1, HOXA10, LUM, SPP1, TGFB2, THBS1, and TMEM47 also serve as effective predictors of poor overall survival outcomes as shown in Fig. 11 .
  • an OCSC signature is analyzed using Connectivity Map (http://www.broadinstitute.org/cmap/) public database, which predicts candidate drugs modulating expression of a specific gene signature.
  • Connectivity Map http://www.broadinstitute.org/cmap/
  • Connectivity Map pattern-matching of the query (e.g. OCSC gene signature) with a reference collection of gene expression profiles from cultured human cells treated with bioactive small molecules.
  • Another example is the Ingenuity Pathway Analysis application described above. Application of Connectivity Map or Ingenuity Analysis with the ovarian CSC signature allows identification of compounds that repress, modulate, or alter targets associated with the OCSC signature.
  • TGF-beta 1 and 2 are associated with OCSC signature and are effective predictors of poor survival.
  • TGF-beta is known to be an important signaling pathway involved in stem cell development, including regulation of epithelial to mesenchymal transition, aberrant TGF-beta regulation may be a key mechanism underlying the role of OCSC in tumor formation and disease progression.
  • application of the Ingenuity Pathway Analysis demonstrates that several OCSC signature biomarkers are regulated by TGF-beta pathway proteins.
  • OCSC biomarkers include ACTA2, ADAM12, COL11A1, COL3A1, COL5A1, COL6A2, CYR61, DCN, FN1, GREM1, LOX, POSTN, SNAI2, SPARC, TAGLN, TIMP3, VCAN, and/or VIM.
  • Upstream TGF-beta patheway regulators are listed in Table 8, along with their corresponding OCSC biomarkers. Each of these biomarkers could serve as targets of therapies that inhibit aberrant TGF pathway function, demonstrating the utility of a OCSC in developing personalized therapeutic strategies. Table 8.
  • OCSC signature allows identification of not only individual targets that interact in a biochemical network, but specific "hub” genes that may amplify and enhance the role of many complementary targets involved OCSC survival. Without being bound by any particular theory, it is believed that repression, modulation, or alteration of these specific "hub” genes may prove to be more effective in dismantling the biochemical machinery underlying cancer pathogenesis, in contrast to disruption of individual targets involved in the growth and development of cancer.
  • fibroblastic growth factor-1 FGF1
  • fibronectin FN1
  • Fig.2D fibronectin
  • Ovarian cancer stem cells are rare cells, perhaps accounting for less than 1% of the total cell population in a clinical sample or cultured cell lines. Most ovarian cancer patients have bulky tumors and/or extensive metastatic spread at the time of diagnosis, and can provide a large volume of tumor and ascites from a single patient. Enriching for OCSC candidates can be performed by first pooling OCSC candidate using single markers (e.g., CD133 or ALDH1), to isolate more abundant populations for analysis. A second approach can be to induce greater numbers of ovarian CSC population by adding BMP2 to cell lines.
  • single markers e.g., CD133 or ALDH1
  • Identifying metabolites specific to CSCs is accomplished through comprehensive, high-throughput metabolic profiling of OCSCs and non-CSCs.
  • ALDH1+CD133+ OCSC candidates and ALDH1-CD133- cell populations are isolated from both surgical specimens and A2780 human ovarian cancer cell line. After sorting, cells are harvested and frozen at -80°C. Metabolites are extracted from frozen cell pellets and analyzed by GC/MS and LC/MS platforms. Metabolites will be identified by comparison to library entries of purified standards or recurrent unknown entities. Welch's two-sample t-tests are used to identify metabolites that differ significantly between OCSCs and non-CSCs. Significantly altered metabolites will be put into the context of biochemical pathways after statistical analysis and data curation.
  • Initial integration of metabolome and transcriptome data is provided by first applying the preliminary expression profiling data establishing an OCSC signature via microarray, as well as qRT-PCR data obtained from the validated OCSC global signature to develop a mechanistic model of signaling in OCSCs.
  • OCSC signature via microarray
  • qRT-PCR data obtained from the validated OCSC global signature
  • enrichment of isolated OCSCs can be performed by pooling sorted cell populations from multiple surgical samples, inducing ovarian CSC population by adding BMP2 to cell lines, or using single markers (e.g., CD133 or ALDH1) to isolate more abundant populations of OCSC candidates including OCSCs.
  • single markers e.g., CD133 or ALDH1
  • OCSC candidates may display some variation in metabolic properties
  • application of additional stem cell markers, such as CD44 to isolate OCSCs allows further comparison of the levels of several key metabolites and transcripts to those in ALDH1+CD133+ cell populations.
  • Solid tumors are now understood to be complex "organs" comprised of not only malignant cells, such as OCSCs, but also non-malignant cells, thus, while OCSCs clearly play an important role in cancer disease progression, it is of paramount interest to identify the relative contribution of OCSCs in the overall context of tumor formation. For example, the relationship amongst OCSC and surrounding stromal cells is not totally understood. Better understanding processes would aid understanding of tumor formation events, as the roles for these cell types in tumor progression and metastasis are not well defined.
  • stroma Various non-malignant cell types are typically lumped together as tumor “stroma”, and include fibroblasts, resident epithelial cells, immune cells, endothelial cells, pericytes, myofibroblasts and various mesenchymal stem cell types (MSCs) from recruited from bone marrow, fat, and connective tissues.
  • MSCs mesenchymal stem cell types
  • Fig. 5 Primary cell lines (C lines) were generated by isolating ovarian surface epithelial cells from p53-/- mice and introducing various combinations of c-myc, K-ras, and Akt oncogenes in vitro ( Fig. 5A ). Primary cell lines were then intraperitoneally injected into nude mice. To establish corresponding metastatic cell lines (T lines), tumor nodules were then isolated from the intestinal lining ( Fig. 5B,5C ).
  • RFP red fluorescent protein
  • MSCs human immortalized mesenchymal stem cells
  • stromal cells derived from the normal ovary and mouse adipocytes
  • GFP green fluorescent protein
  • fibroblasts for example, distinguishing between upregulated genes in malignant cells or instead, are the result of cells recruited by the tumor, such as fibroblasts, pericytes, immune cells, and bone marrow or fat MSCs
  • human ovarian cancer cell line SKOV3-GFP are used for intraperitoneal injection.
  • Application of human- and mouse-specific PCR primers for selected genes that overlap between the human and mouse i.e. COL11A1, CXCL12, and POSTN
  • COL11A1, CXCL12, and POSTN allows determination of whether the signature in the tumor is of human or mouse origin.
  • biomarkers originating from human cancer cells would result in PCR products with human primers but not mouse primers.
  • biomarkers originating in mouse cells recruited to the tumor would lead to an increase in PCR products with mouse primers, but not human primers.
  • stromal cells Another helpful insight on the contribution of stromal cells is provided by three different types of RFP-labeled stromal cell lines of human origin: human MSCs, fibroblasts, and stromal cells from the normal ovary ( Fig. 6 ). Each of these cell lines can be co-injected with the C11-GFP mouse ovarian cancer cell line into mice for the development of carcinomatosis.
  • the presence of RFP-labeled stromal cells within the tumors will be determined by immunofluorescent visualization of frozen tumor sections and by fluorescence cell sorting.
  • the expression levels of the biomarkers will be determined using human- and mouse-specific PCR primers as described above.
  • stromal cells enhance tumor growth in xenografts, this approach identifies specific contributions of certain stromal cell types to tumor formation.
  • FACS fluorescence cell sorting
  • RNA isolation for expression profiling using qRT-PCR.or human- and mouse-specific microarrays. This approach identifies altered gene expression patterns that are involved in lineage-specific differentiation, and can further identify the specific contribution of differentiated adipocytes, osteocytes and chondrocytes to tumor formation. Further, co-injection with ovarian cancer cells further answers the question of whether the presence of cancer cells affects differentiation status (i.e. over-representation of genes involved in de-differentiation).
  • Ovarian cancer disease signature molecular subgroups, patient prognosis and survival
  • OCSC ovarian cancer disease signature
  • OCDS OCDS along different experimental designs allows one to capture the breadth of molecular heterogeneity of gynecological cancers, particularly as cancer is a multi-etiological disease likely to arise from a variety of biological factors.
  • An example of this data is provided by analysis of The Cancer Genome Atlast (TCGA) dataset, which includes ovarian serous cystadenocarcinoma. Analysis of this data by Orsulic et al. identified a 86 OCDS biomarker signature presented in Table 3, as associated with poor survivial.
  • a second example includes identification of individual genes that correlated with prognosis, such as the identification of genes associated with suboptimally debulked tumor, using the data set by Bonome et al. Bonome et al., (2008) A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer. Cancer Res,68:5478-86 . Prognostic classification of this data, such as poor patient response to therapeutic treatment, identified a 68 OCDS biomarker signature, as analyzed by Cui et al. and shown in Table 4.
  • Variation in experimental design is likely to best capture the molecular heterogeneity of the disease, and aid the understanding the cellular context of the poor-prognosis gene signature and disassembling the gene signature network.
  • the OCDS provided by each study outperforms the predictive power of individual genes.
  • Ovarian cancer fixed signature (OCFS)
  • OCDS ovarian cancer disease signatures
  • Table 1 ovarian cancer disease signature
  • Table 2 ovarian cancer disease signatures
  • three expression studies provided OCDS focused on gene clustering associated with: 1) poor survival (Orsulic et al .), 2) poor prognosis (Cui et al .), and 3) periostin (POSTN) co-expressing genes (Karlan et al .)
  • POSTN periostin
  • a composite of OCDS biomarkers helps identify the overlapping biomarkers which mayidentify core programming as associated with ovarian cancer, while eliminating outliers.
  • OCFS ovarian cancer fixed signature
  • OCFS ovarian cancer fixed signature
  • the application of different data sets and different approaches to OCDS identification revealed preliminary OCFS indicating poor-prognosis, as substantially overlapping between the four studies ( Fig. 7A ). It is notable that one gene (COL11A1) is present in all four signatures, while 21 genes listed in Fig. 7B are present in at least three of the four poor prognosis OCDS.
  • a 21-gene OCFS representing candidates present in three of four studies provides an preliminary "fixed signature" for further refinement.
  • These 21 genes include ACTA2, ADAM12, AEBP1, COL11A1, COL3A1, COL5A1, COL6A2, CYR61, DCN, FN1, GREM1, LOX, LUM, POSTN, SNAI2, SPARC, TAGLN, THBS2, TIMP3, VCAN, VIM.
  • a 101-gene OCFS represents candidates present in two of four studies, thereby allowing one to encompass heterogeneity of molecular subtypes.
  • 101-genes include ACTA2, ACTG2, ADAM12, ADH1B, AEBP1, ALDH1A1, ALDH1A2, ANGPTL2, ASPN, C1QTNF3, CALD1, CAV1, CD36, CD248, CNN1, COL10A1, COL11A1, COL1A2, COL1A1, COL3A1, COL5A1, COL5A2, COL6A2, COLEC12, COMP, CTSK, CXCL14, CXCL12, CYP1B1, CYR61, DCN, DOCK11, DUSP1, ECM1, EDNRA, EFEMP1, EGR1, ELL2, EPYC, FAP, FBLN1, FBLN2, FBN1, FGF1, FN1, FOSB, GPNMB, GREM1, GUCY1A3, HBB, HOXA7, IGFBP5, IL6, IL7R, INHBA, ITGBL1, LAMA4, LAMB1, LOX, LOXL1, LUM, MAL
  • any new prognostic assay will depend on whether it provides therapeutically relevant information that is superior to the well-validated clinical variables.
  • the objective of our statistical analysis is to correlate progression-free survival (PFS) with gene expression to estimate if a gene expression signature can provide prognostic information beyond the existing standard in clinical practice. Optimization and validation of the 21-gene OCFSwill be done in Three Phases as described below.
  • Ovarian cancer fixed signature (OCFS) derivation Ovarian cancer fixed signature
  • OCFS ovarian cancer fixed signature
  • SPC Supervised Principal Components regression
  • Ovarian cancer fixed signature (OCFS) validation Ovarian cancer fixed signature validation.
  • Additional examples of analyzing publicly available datasets for signature validation demonstrates the capability of individual biomarkers to predict specific clinical outcomes. For example, analysis across multiple data sets identified several biomarkers,that served as strong predictors of specific clinical outcomes. As shown in Fig. 9 , high expression of COL3A1, DCN, LUM, SPARC, VCAN, COL11A1, COL5A1 and POSTN were individually predictive of poor progression-free survival when highly expressed, as shown by analysis across multiple data sets encompassing a total of 1,107 patients. Likewise, as shown in Fig.
  • biomarkers 10 other clinical parameters, such as poor overall survival, were effectively predicted by high expression of individual biomarkers, COL3A1, DCN, LUM, SPARC, TIMP3, and VCAM, as shown by analysis across multiple datasets encompassing 1,339 patients.
  • Combination of biomarkers, as shown in Fig. 10H and 10I demonstrates the enhanced predictive power of using a panel of biomarkers, wherein high expression is highly predictive of overall survival.
  • the 21-gene signature was not enriched when the primary cell lines were compared to the primary cell lines or when the metastatic cell lines were compared to the metastatic cell lines (i.e. comparison of 3 myc+Kras vs. 3 Akt+myc cell lines).
  • the presence of the 21-gene signature in mouse primary vs. metastatic cell lines indicates that a process similar to human ovarian cancer progression occurs in the mouse model.
  • this mouse model can be used to study the cellular context of the biomarkers.
  • TCGA high-grade, advanced-stage, primary serous ovarian carcinoma samples
  • GSE26712 dataset
  • Karlan dataset
  • the Inventors evaluated the potential predictive value of the 10-gene signature in the three discovery datasets and one independent validation dataset by comparing survival in patient groups with 'high' and 'low' expression of the 10 genes.
  • IPA Ingenuity Pathway Analysis
  • the Inventors treated the human ovarian stromal cell line TRS3 and the ovarian cancer cell line OVCAR3 with TGF ⁇ 1 and measured mRNA expression of the 10 poor outcome genes before and after TGF ⁇ 1 treatment.
  • OCFS genes are enriched during ovarian cancer progression by using another method of mRNA detection (qPCR) in an independent patient cohort that included 8 normal ovaries, 30 primary serous ovarian tumors, and 29 metastatic serous ovarian tumors from the Women's Cancer Program Biorepository ( Fig. 21 ).
  • Inventors conducted an unbiased global identification of genes that are differentially expressed between primary tumors and metastases using nine matched pairs of primary ovarian tumors and omental metastases (GSE30587 dataset).
  • the top 20 gene probes that exhibited increased expression in metastases are ranked according to statistical significance in Fig 3B .
  • This analysis showed a marked overlap between our poor prognosis signature genes and genes that are enriched in metastases ( Fig. 14B ).
  • One of our signature genes, COL11A1 was identified as the most statistically significant differentially expressed gene in the nine matched pairs of primary and metastatic tumor samples ( Fig. 14B ).
  • Fig. 14C shows COL11A1 mRNA expression values in matched pairs of primary ovarian tumors and omental metastases in the nine matched tumor pairs.
  • COL11A1 is a marker of tumor progression
  • the Inventors relied on in situ hybridization in 10 patients with "triplet" samples (primary ovarian cancer, concurrent metastasis, and recurrent/persistent metastasis) and eight additional patients with matched primary ovarian cancer and recurrent/persistent metastatic tumor.
  • COL11A1 expression increased in the recurrent/persistent metastasis compared to the matched primary ovarian tumor ( Fig. 15A ,B and Fig. 21 ).
  • COL11A1 exhibited the lowest levels in primary ovarian cancer samples, medium levels in concurrent metastases, and highest levels in recurrent/persistent metastases ( Fig. 15A ).
  • Representative in situ hybridization images for Patient 1 are shown in Fig. 15B .
  • serial sections from primary ovary, concurrent metastatic, and recurrent/persistent metastatic tumors were stained for the COL11A1 protein using immunohistochemistry.
  • COL11A1 protein levels and pattern of expression in these serial sections were consistent with COL11A1 RNA levels and pattern of expression ( Fig. 15B ); however, in situ hybridization provided a higher-resolution signal at a cellular level.
  • COL11A1 expression was predominantly confined to stromal cells although rare clusters of positive epithelial cells were observed in some tumors ( Fig. 15C ).
  • COL11A1 was specifically expressed in the intra/peri-tumoral stromal cells while stromal cells >1 mm from the epithelial tumor cells were always negative ( Fig. 15D and Fig. 22 ).
  • COL11A1 has a functional role in tumor progression
  • a mouse tumor xenograft model with A2780 human ovarian cancer cells was used. Despite their epithelial morphology, these cells exhibit a mesenchymal-like expression profile, including low levels of E-cadherin and high levels of N-cadherin proteins (Ruby Huang, Cancer Science Institute of Singapore, personal communication).
  • A2780 cells have relatively high levels of endogenous COL11A1 and thus may represent the small subset of COL11A1-expressing epithelial tumor cells that the Inventors observed in patient tumors ( Fig. 15C ).
  • COL11A1 expression in A2780 cells was silenced using shRNA lentiviral particles.
  • the OCFS 10-gene signature is robust in its ability to predict poor survival as demonstrated in two large validation datasets consisting of 260 ovarian cancer patient samples and 1,058 pooled ovarian cancer patient samples.
  • individual genes or groups of genes from the OCFS 10- gene signature including COL11A1, POSTN, SNAI2, THBS2 and TIMP3, have also been associated with poor survival in other solid tumors including breast, colorectal, lung, oral, and head and neck carcinomas as well as melanoma, suggesting that expression of this signature is not specific to ovarian cancer but might characterize aggressive behavior across cancer types.
  • OCFS 10-gene signature Another major strength of the OCFS 10-gene signature is its clear biological relevance to cancer progression.
  • Previously identified gene signatures in ovarian cancer consist of genes that are involved in many diverse biological processes, making it difficult to assess their biological relevance or functional role in cancer progression.
  • All 10 of the OCFS signature genes are present in the 351-gene signature that was identified as upregulated in invasive ductal carcinoma (IDC) when compared to noninvasive ductal carcinoma in situ (DCIS), supporting the important role of these genes in early local invasion.
  • IDC invasive ductal carcinoma
  • DCIS noninvasive ductal carcinoma in situ
  • the Inventors further showed that the 10 signature genes are highly enriched in metastases and that knockdown of one of these genes, COL11A1, results in reduced cell migration, invasion, and tumor progression, suggesting that collagen remodeling could be important in ovarian cancer progression and metastasis.
  • the higher expression of 10 genes in metastasis does not appear to be due to a higher stroma-to-t
  • tumor samples from the TCGA dataset were selected to have >70% tumor cells.
  • the Inventors did not observe different expression levels of the epithelial marker, EPCAM, and the stromal marker, vimentin, in metastatic tumors compared to primary tumors ( Fig. 14A ), indicating that the stroma-to-tumor ratio is not significantly different between samples of primary tumors and metastases.
  • in situ hybridization results showed that regardless of the overall amount of stroma in tumor sections, COL11A1 expression was confined to intra-/peri-tumoral stromal cells and rare foci of tumor epithelial cells, while stromal cells that were >1 mm from epithelial tumor cells were completely negative ( Fig. 15B-D ).
  • COL11A1 is a specific marker of carcinoma-associated fibroblasts (CAFs) and possibly cancer cells that are undergoing EMT.
  • CAFs carcinoma-associated fibroblasts
  • Collagen-rich stroma is thought to maintain tissue architecture and, under normal conditions, serve as a barrier to epithelial cell migration. However, when modified by cancer cells, collagen-rich stroma can promote tumor progression. Enhanced collagen deposition and cross-linking has been shown to increase breast cancer risk. Increased levels of LOX, an enzyme responsible for collagen cross-link formation, result in increased collagen stiffness. POSTN also promotes collagen cross-linking by interacting with BMP-1 to enhance the proteolytic activity of LOX, which results in the reorganization of loose connective tissue into linear tracks of fibers that promote chemotaxis of tumor cells). Furthermore, increased collagen deposition and remodeling increases interstitial pressure, thereby severely compromising the efficacy of drug delivery. Of particular interest, an increase in collagen expression and remodeling has been associated with cisplatin resistance in ovarian cancer, suggesting that cisplatin resistance might be one of the factors contributing to poor survival.
  • the clinically-relevant strength of the OCFS 10-gene signature is that it can be not only used as a biomarker to identify patients with poor outcome but also as a guide to individualize their therapy.
  • POSTN an extracellular matrix protein that is highly expressed in late-stage ovarian cance, is thought to play a role in metastatic colonization by forming a niche for cancer stem cells.
  • Treatment with a POSTN-neutralizing antibody led to a significant decrease in ovarian tumor growth and metastasis in a mouse model.
  • inhibiting LOX by treatment with ⁇ -aminopropionitrile, neutralizing antibodies, or RNA interference inhibited tumor metastasis in xenograft and transgenic mouse models.
  • the COL11A1 knockdown result suggests that targeting collagen might be an effective approach to preventing ovarian cancer progression and metastasis.
  • CMPs collagen mimetic peptides
  • MMP matrix metalloproteinase
  • TGF ⁇ 1 signaling activity was reported in patients with metastatic ovarian cancer and the antibody against TGF ⁇ was shown to be effective in suppressing metastasis in a preclinical model of ovarian cancer.
  • TGF ⁇ 1 inhibitors in phase I/II clinical trials. It will be important to test the effectiveness of these agents as inhibitors of ovarian cancer progression and metastasis as single agents or in combination with chemotherapy.
  • OCFS 10-gene signature assay will effectively enhance physicians' abilities to identify patients with a high likelihood of recurrence.
  • Fig. 8A shows the triage map of current clinical practice and the point of care at which our gene signature test will be of clinical value.
  • a Nanostring assay can be developed for the detection and quantification of the gene signature transcripts. This system is favored for accurate detection of transcripts in paraffin samples without relying on cDNA synthesis or PCR amplification. Assay development paraffin blocks from patients linked to a detailed correlative database with clinical variables, including recurrences. Cox regression of the log hazard ratio on a covariate with a standard deviation of 1.5 based on a sample of 100 observations achieves 85%, and 95%, power at a 0.05 significance level to detect a regression coefficient (log hazard ratio) equal to 0.2 and 0.25 respectively. Sample size can adjusted for an anticipated event rate of 80%.
  • Model performance can be assess with (1) calibration measures to study the agreement between the observed outcome frequencies and predicted probabilities, (2) discrimination (classification) measures to distinguish subjects with different outcomes at each time cutoff point, and (3) prediction accuracy.
  • Model calibration will be studied with a calibration curve in which observed frequencies are plotted against predicted probabilities. Ideally, if the observed frequencies and predicted probabilities agree over the whole range of probabilities, the plot will show a 45 degree line with an intercept of 0 and slope of 1. Therefore, Chi-square distribution with 2 degrees of freedom will be used to test the null hypothesis that the intercept is 0 and the slope is 1. Additionally, one can also use the Hosmer-Lemeshow (H-L) goodness-of-fit test for calibration validation. Both equal sizes and equal prediction-intervals will be used to group the patients into subgroups.
  • H-L Hosmer-Lemeshow
  • Cox regression based risk model Especially for the Cox regression based risk model, one can categorize patients into subgroups based on their distribution of relative risk with the Kaplan-Meier method and calculate the goodness-of-fit for calibration validation.
  • AUC c-statistics
  • F-measure will be computed to evaluate the prediction ability of the risk models given a cutoff time.
  • the Global AUC summary (GAUCS) will also be used to estimate the performance of Cox regression based risk models.
  • the OCFS 10-gene signature is enriched in metastatic ovarian cancer.
  • the Inventors evaluated the expression levels of the 10-gene signature in Fig. 12A in primary and metastatic serous ovarian tumors.
  • the GSE12172 dataset which includes 74 high-grade serous primary ovarian tumors and 16 unpaired metastatic tumors, higher expression levels of the 10 genes were observed in the metastatic tumors. This observation was further validated using qPCR in an independent patient cohort that included 8 normal, 30 primary, and 29 unpaired metastatic serous ovarian cancer samples from our biorepository.
  • OCFS 10 signature genes were not expressed in normal ovaries, but their expression was enriched in primary tumors and even further enriched in metastases. Importantly, this indicates that during tumor progression, the cell population expressing the signature genes is enriched or the process resulting in the expression of the signature genes is intensified.
  • the signature genes are expressed in both MSCs and CSCs.
  • the Inventors searched the tissue/cell-specific transcripts in the TranscriptoNet database, which consists of >500 normal tissues and cell types that were isolated from >50 different organs by microdissection (i.e. epithelial cell of the endometrium) or by FACS using specific cell markers (i.e. 27 distinct blood cell types). Since the signature genes are highly co-regulated, the presence or absence of the signature was immediately apparent.
  • the signature was present in only 3 normal cell types: undifferentiated pre-adipocytes (but not differentiated pre-adipocytes) from visceral, subcutaneous and omental fat; cardiomyocytes; and bone marrow MSCs.
  • the common denominator to the 3 cell types is that they are all MSCs.
  • OCSC OCDS
  • OCFS signatures 1) as derived from epithelial cancer cells that de-differentiate into CSCs as derived from MSCs that are actively recruited to the tumor from fat and/or bone marrow or from local fibroblasts that de-differentiate into MSC 2) as derived process-specific mechanism, such as any cell undergoing de-differentiation or trans-differentiation into CSCs or MSCs will express the signature genes.
  • process-specific mechanism such as any cell undergoing de-differentiation or trans-differentiation into CSCs or MSCs will express the signature genes.
  • COL11A1 To identify the cell-of-origin for one of the signature genes, one can focus on COL11A1 as this gene is not expressed in most normal tissues but its expression is highly elevated in most cancer types in comparison to their respective normal tissue.
  • the ratio of COL11A1-positive to COL11A1-negative cells also increased in metastatic vs. primary tumors.
  • metastatic tumors contained more COL11A1-expressing cells, although it is unclear if these cells were recruited to the tumor or represented resident cells that converted into a COL11A1-expressing phenotype.
  • COL11A1 is expressed in the stromal, rather than the epithelial, component of the tumor, however, the Inventors cannot exclude the possibility that the stromal cells are derived from epithelial cells through EMT.
  • COL11A1 is expressed in stromal cells in close proximity ( ⁇ 1mm) to malignant epithelial cells while distant stromal cells and stromal cells encapsulating the tumor were negative, which is consistent with the idea that the COL11A1-expressing stromal cells are derived locally rather than recruited from distant sites.
  • a helpful determination the cell-of-origin can rely on a cell tracking system, such as labeled human cancer cell lines as a source of malignant cancer cells and a mouse host as a source of stromal cells.
  • a cell tracking system such as labeled human cancer cell lines as a source of malignant cancer cells and a mouse host as a source of stromal cells.
  • a previously generated a mouse model by the Inventors in which defined genetic alterations can be introduced into ovarian surface epithelial cells can be applied for such studies.
  • When such cells are implanted under the ovarian bursa, they give rise to primary ovarian tumors as well as metastatic nodules embedded into the peritoneal serosal surfaces, such as the peritoneal wall, intestinal lining, and omentum ( Fig. 23B ).
  • Expression profiling revealed that many of the signature genes were enriched in the mouse metastatic tumors, with COL11A1 and POSTN showing the highest fold increase in metastatic vs. primary ovarian tumors ( Fig. 23B ). This is significant because COL11A1 and POSTN are among the genes that exhibit the highest fold increase in human metastatic vs. primary tumors, indicating that a process similar to human ovarian cancer metastatic progression occurs in the mouse model. As a result, this mouse model can be used to study the cellular context of the signature genes.
  • the cell-of-origin for the signature transcripts may not be obvious as the majority of the signature genes encode secreted extracellular matrix proteins.
  • Research groups who have identified similar collagen-remodeling gene sets as predictors of poor outcome in breast cancer have attempted to determine their cell-of-origin using laser capture of tumor epithelial and stromal cells but have reached contradictory conclusions.
  • OCSC OCSC
  • OCDS OCFS signatures
  • multiple tumor types including breast, colon, and lung cancer
  • multiple metastatic locations including local invasion in breast cancer and metastatic dissemination to various intraperitoneal organs in ovarian cancer
  • Such signatures can be either specific to a certain cell type that is present in diverse tumor types (i.e. recruited macrophages or MSCs) or to a common biological process that occurs in diverse cell types (i.e. de-differentiation of cancer cells into CSCs or trans-differentiation of fibroblasts into myofibroblasts or MSCs).
  • MSC macrophages
  • de-differentiation of cancer cells into CSCs or trans-differentiation of fibroblasts into myofibroblasts or MSCs To accurately define the therapeutic target, it is important to identify the cellular context of the poor-prognosis signature gene expression.
  • he signature genes are expressed in a specific cell type derived from either malignant cancer cells or host stromal cells. This can be determined using labeled cells of human and mouse origin in a mouse model of metastatic ovarian cancer progression.
  • a key question is whether various signatures are derived from malignant cancer cells or host stromal cells. Many of the genes in the signature are known inducers of EMT and their expression in cancer cells may make these cells indistinguishable from non-tumor mesenchymal cells.
  • Red fluorescence protein (RFP)-labeled OVCAR3 cells will be compacted using a hanging-drop technique and implanted under the ovarian bursa of nude mice.
  • mice When the mice develop carcinomatosis, primary ovarian tumors and metastatic tumors will be harvested for 1) fluorescence activated cell sorting (FACS), 2) immunofluorescence (IF), and 3) qPCR.
  • FACS fluorescence activated cell sorting
  • IF immunofluorescence
  • IF immunofluorescence
  • POSTN BioVendor, RD172045100
  • qPCR analysis will be used to determine the mRNA levels of COL11A1 and POSTN in RFP+ and RFP- cells of the primary and metastatic tumors.
  • human- and mouse-specific PCR primers will provide a second layer of assurance that the signature genes are derived from human or mouse cells.
  • Levels of COL11A1 and POSTN can be elevated in human and mouse metastatic vs. primary tumors. If the signature originates from human cancer cells, enrichment in the relative number of RFP+ cells expressing COL11A1 and POSTN in IF analysis and/or an increase of COL11A1 and POSTN expression levels (with human but not mouse PCR primers) in metastatic vs. primary RFP+ cells would be observed. If the signature originates from mouse host cells, enrichment in the relative number of RFP- cells expressing COL11A1 and POSTN by IF and/or an increase of COL11A1 and POSTN in metastatic vs. primary RFP- cells by qPCR (with mouse but not human primers) would be observed.
  • COL11A1 and POSTN are elevated in both human RFP+ and mouse RFP- fractions, it is suggested that both human cancer cells and mouse nonmalignant cells upregulate the expression of COL11A1 and POSTN during the process of metastasis.
  • cancer cells can generate CSCs through EMT.
  • Patient outcome and drug resistance have also been linked to the properties of CSCs.
  • the signature is enriched in patients with poor prognosis because their tumors contain more CSCs.
  • COL11A1 and POSTN are among the top upregulated genes in OVCAR3 cell spheroids, which are enriched for CSCs.
  • OVCAR3 xenografts To determine whether COL11A1 and POSTN are specifically overexpressed in the CSC population in OVCAR3 xenografts, one can isolate RFP+/ALDH1+ and RFP+/ALDH- tumor cells and compare expression of COL11A1 and POSTN in the two cell populations by qPCR and IF.
  • signatures as arising from carcinoma-associated stroma may be of bone marrow origin or adipose tissue.
  • Bone marrow from male C57BL6 mice expressing GFP under the ubiquitin promoter mice can be isolated and transplanted into lethally-irradiated female C57BL6 mice expressing RFP under the chicken albumin promoter. Successful engraftment can be determined after 4 weeks by >95% GFP expression in the peripheral blood and bone marrow (and death of control mice without bone marrow transplants).
  • mice can be orthotopically implanted with syngeneic C57BL6 p53-/-;HA-myc;H-ras mouse ovarian cancer cells, which the Inventors recently generated and tested in mice.
  • Primary and metastatic tumors typically form 4-5 weeks after implantation of hanging drop-compacted cells into female C57BL6 mice.
  • the primary and metastatic tumors are then isolated and analyzed by FACS for the proportion of the cancer cells (HA+), recruited non-bone marrow cells (RFP+), and recruited bone marrow cells (GFP+).
  • HA+ cancer cells
  • RFP+ recruited non-bone marrow cells
  • GFP+ bone marrow cells
  • expression of COL11A1 and POSTN in metastatic tumors in the GFP+ cells indicates the bone marrow/adipose tissue origin of the signature while increased expression in the RFP+ cells will indicate that the signature originates in mouse cells other than the bone marrow/adipose tissue ( Fig. 24 ).
  • An increase in both GFP+ and RFP+ cell populations indicates that both bone marrow/adipose tissue- and non-bone marrow/adipose tissue-derived cells contribute to the signature gene expression. If the signature genes are solely derived from non-cancer cells, there will be no increase in the signature genes in HA+ cells.
  • COL11A1 Based on the described results, the Inventors have successfully targeted COL11A1 for reduction of tumor metastasis.
  • One common feature of the signature genes is their extracellular matrix localization and involvement in collagen remodeling, suggesting that collagen remodeling might be a common biological process that contributes to cancer progression and poor overall survival.
  • the Inventors selected COL11A1 because this gene is highly expressed in most solid tumors but, importantly, not expressed in most normal tissues.
  • COL11A1 expression was silenced using shRNA in the A2780 human ovarian cancer cell line, which has high levels of endogenous COL11A1. Knockdown of COL11A1 did not affect cell proliferation ( Fig. 25A ), however, it resulted in significantly decreased cell migration and invasion in vitro ( Fig. 25B ).
  • the Inventors injected nude mice with scrambled shRNA (sh-scr) A2780 cells or COL11A1-specific shRNA (sh-COL11A1) A2780 cells. After 14 days, the mice injected with sh-scr A2780 cells developed large disseminated tumors while the mice with sh-COL11A1 A2780 cells developed small focal tumors ( Fig. 25C , D ). This result suggests that targeting collagen might be an effective approach to preventing ovarian cancer invasion and metastasis.
  • TGF ⁇ plays a crucial role in almost every aspect of tumor progression and metastasis.
  • increased TGF ⁇ 1 signaling activity was reported in metastatic ovarian tumors in comparison to matched primary ovarian tumors and the antibody against TGF ⁇ was shown to be effective in suppressing metastasis in preclinical models of ovarian cancer.
  • TGF ⁇ inhibitors in phase I/II clinical trials. It will be important to test the effectiveness of these agents as inhibitors of ovarian cancer progression and metastasis as single agents or in combination with chemotherapy.
  • the Inventors applied Ingenuity Pathway Analysis to the 61 signature genes in Fig. 12A and identified TGF ⁇ 1, TGF ⁇ 2, TGF ⁇ 3, SMAD3, and SMAD7 as top transcription factors regulating expression of the signature genes.
  • TGF ⁇ 1 TGF ⁇ 2, TGF ⁇ 3, SMAD3, and SMAD7 as top transcription factors regulating expression of the signature genes.
  • the Inventors treated the human ovarian cancer cell line OVCAR3 (exhibits low endogenous levels of the signature genes) with TGF ⁇ 1 and measured mRNA expression of the genes before and after TGF ⁇ 1 treatment.
  • the 10 OCFS genes showed slightly different induction times in the presence of TGF ⁇ 1.
  • TGF ⁇ inhibitor TGF ⁇ inhibitor
  • 1D11 TGF ⁇ neutralizing antibody
  • TGF ⁇ signaling is completely impaired in stromal cells, such as mice with a conditional knockout of the TGF ⁇ type II receptor.
  • TGF ⁇ signaling is completely impaired in stromal cells
  • mice with a conditional knockout of the TGF ⁇ type II receptor One can have inject the described syngeneic mouse ovarian cancer cells i.p. into Tgfbr2 KO mice.
  • preliminary results indicate that growth of ovarian cancer is inhibited upon knockout of the TGF ⁇ type II receptor in mouse stromal cells. Additional experiments can be done to verify this result and determine the effect of the stromal TGF ⁇ type II receptor knockout on the proportion of CSCs and recruitment of stromal cells to the tumor.
  • TGF ⁇ signaling Both pro- and anti-tumorigenic activities have been documented for TGF ⁇ signaling in different cancer types, based on the observed increase of TGF ⁇ 1 signaling in metastatic ovarian cancer, the suppression of metastasis upon TGF ⁇ 1 inhibition in mouse models of ovarian cancer, and our observation that TGF ⁇ induces expression of the signature genes, one anticipates that inhibition of TGF ⁇ signaling by pharmacologic (A83-01), biologic (1D11) or genetic (Tgfbr2 KO mice) means will have a negative effect on the expression of the signature genes with the resultant loss of a nurturing environment for CSCs and an increased chemosensitivity to cisplatin.
  • pharmacologic A83-01
  • biologic (1D11) or genetic Tgfbr2 KO mice
  • collagen-rich stroma maintains tissue architecture and serves as a barrier to epithelial cell migration, it can turn into a collaborator of cancer progression when modified by malignant cancer cells.
  • enhanced collagen deposition and cross-linking is associated with mammographic density, which is one of the greatest risk factors for breast cancer.
  • collagen I is enriched and aligned at the stromal border in breast tumors and changes in collagen I organization are associated with poor prognosis in breast cancer.
  • a rodent model with increased collagen deposition due to altered collagen degradation exhibited increased mammary tumor formation and progression to metastasis.
  • POSTN also promotes collagen cross-linking by interacting with BMP-1 to enhance the proteolytic activity of LOX. This results in the reorganization of loose connective tissue into linear tracks of fibers that serve as highways to promote chemotaxis of tumor cells. Indeed, breast cancer studies using live imaging have demonstrated that cancer cells migrate on collagen fibers in areas enriched in collagen. Collagen stiffness has also been shown to regulate stem cell differentiation and EMT.
  • collagen deposition and remodeling increases interstitial pressure, which severely compromises the efficacy of drug delivery.
  • remodeling of the ECM through overexpression of COL6A3 in tumor cells was shown to contribute to cisplatin resistance in ovarian cancer.
  • the reduction in collagen stiffness can repress the malignant behavior of mammary epithelial cells and administration of collagenase increased the uptake and distribution of monoclonal antibodies in a mouse model of osteosarcoma.
  • collagen degradation may be an effective approach to targeting the gene signature network. It remains to be determined whether ECM stiffness in cancer may be restored to normal and how such a restoration may benefit treatment prognosis.
  • miRNA micro RNA
  • the miRNA-29 family specifically miR-29b, was recently shown to inhibit tumor metastasis by targeting a network of collagen-remodeling genes.
  • miRNAs have not yet been fully adapted for use in the clinic, significant progress has been made in improving the specificity of delivery.
  • Fig. 8A shows an exemplary triage map of current clinical practice and the point of care at which various OCSC, OCDS, and OCFS signatures provide specific clinical guidance value.
  • qRT-PCR quantitative real-time PCR
  • microarrays are not cost-effective for use in the clinic.
  • a variety of qRT-PCR platforms can be utilized for detection and quantification of OCSC, OCDS, and OCFS transcripts derived from ovarian cancer samples. This includes ABI TaqMan® OpenArray® Real-Time PCR ( Fig. 8B ). In each instance, it may be highly desirable to design arrays such that custom-selected probes for each gene in the signature are represented in quadruplicates measurements, thereby eliminating outlier measurements and potential false-positive amplifications.
  • a variety of housekeeping genes such as GUSB, PPIA, TBP, RPLP0, RPL4, 18S, ACTB, and GAPDH, each provide baseline expression transcripts measurements that provide assay normalization. Examples of normalization measurements include delta cycle-threshold measurements.
  • qRT-PCR is performed on 112 patient samples that previously used for microarray analysis in one of the three discovery data sets used for identification of periostin (POSTN)-coexpressing genes.
  • This particular data set includes a correlative clinical database with approximately 300 clinical variables, including patient history, symptoms, treatment, other cancer history, surgeries, recurrences and survival status.
  • RNA will be extracted from both frozen and paraffin-embedded primary ovarian cancer samples and reverse transcribed to obtain cDNA.
  • Phase 1 After normalization, the expression of various OCSC, OCDS, and OCFS biomarkers are further correlated with clinical outcomes, including progression-free survival (PFS) and overall survival (OS). From Phase 1, the Inventors expect to have approximately 10 genes that are optimized for the prediction of clinical outcomes.
  • the methods described in Phase 1 will be employed to derive a gene signature for each time to event endpoint, PFS and OS.
  • Model validation will be performed using each of the methods described in Phase 2 by randomly splitting the set of 112 patient samples into a 2/3 training set and a 1/3 validation set.
  • the high levels of statistical robustness may limit the maximum number of genes that are highly correlated with overall survival that overlap when using any of the four statistical methods described in Phase 1 is less than 8. In such instances, one can identify the largest subset of genes whose expressions are available and common to at least three studies and use that particular set for identifying the signature.
  • biomarkers including COL11A1, LOX, POSTN, THBS2, and VCAN
  • the inventors have validated in pre-clinical models two of the biomarkers, CXCL12 and periostin (POSTN), as suitable targets for ovarian cancer treatment. These results demonstrate that OCSC, OCDS, and OCFS biomarkers are not only predictors of poor survival but also play important roles in tumor progression and thus could be used as therapeutic targets.
  • ovarian cancer To test the effectiveness of targeting individual biomarkers in a mouse model of ovarian cancer, one can identify genes that are significant in ovarian cancer progression, functional assays are necessary. Such assays should identify if the candidate genes are sufficient and required for tumor maintenance and progression and, thus, could be used for targeted therapy. One can functionally characterize the contribution of selected genes to ovarian cancer pathogenesis using a genetically relevant mouse model. Also, one can test inhibitors and small molecules specific for candidate signature network genes for their effectiveness to reduce cancer progression and increase chemosensitivity to cisplatin.
  • EMT epithelial-mesenchymal transition
  • a similar approach may rely on antibodies, nucleic acids, pepttides and/or proteins, or other biologics to severely defect OCSC function.
  • small interfering RNA (siRNA) or short-hairpin RNA (shRNA) knockdown of FGF1 and FN1 gene expression transcript could achieve these effects.
  • Another example includes targeting of other adhesion markers, such as L1CAM.
  • an OCSC-specific antibody could be applied, which targets antigens that could be uniquely, or highly expressed in OCSCs. Examples include antibodies specific for chemoresistance mediator, ABCC5, or surface markers, CD24, CD44, CD117, CD133 or ALDH.
  • collagen-rich stroma maintains tissue architecture and serves as a barrier to epithelial cell migration, it can turn into a collaborator of cancer progression when modified by malignant cancer cells.
  • enhanced collagen deposition and cross-linking is associated with mammographic density, which is one of the greatest risk factors for breast cancer.
  • mammographic density which is one of the greatest risk factors for breast cancer.
  • collagen deposition contributes to cancer formation, as an animal model with increased collagen deposition due to altered collagen degradation exhibit increased mammary tumor formation and progression to metastasis.
  • collagen deposition increased levels of LOX, an enzyme responsible for collagen cross-link formation, results in increased collagen stiffness.
  • POSTN also promotes collagen cross-linking by interacting with BMP-1 to enhance the proteolytic activity of LOX. This results in the reorganization of loose connective tissue into linear tracks of fibers that serve as highways to promote chemotaxis of tumor cells. Indeed, breast cancer studies using live imaging have demonstrated that cancer cells migrate on collagen fibers in areas enriched in collagen. Collagen stiffness has also been shown to regulate stem cell differentiation, although the molecular mechanisms of how collagen composition regulates decisions between stem cell expansion and differentiation are not well understood. Furthermore, increased collagen deposition and remodeling increases interstitial pressure, which severely compromises the efficacy of drug delivery.
  • collagen stiffness can repress the malignant behavior of mammary epithelial cells, and administration of collagenase increased the uptake and distribution of monoclonal antibodies in a mouse model of osteosarcoma.
  • collagen degradation by may be an effective approach to targeting the gene signature network. Therefore, one can test the effectiveness of collagenase in reducing ovarian cancer growth and/or increasing sensitivity to chemotherapy.
  • Collagenase clostridium histolyticum XIAFLEX
  • XIAFLEX can be used for intraperitoneal injections because this drug has been approved by the FDA for Dupuytren contracture (clinicaltrials.gov; NCT00528606) and is commercially available.
  • biomarkers will be effective in reducing tumor growth and/or increasing sensitivity to cisplatin. If the main mechanism by which the biomarkers contribute to tumor progression is collagen deposition and cross-linking, loosening the collagen matrix with collagenase may be the most effective way to inhibit the entire network of biomarkers.
  • TGF- ⁇ signaling antagonist agents There are many TGF- ⁇ signaling antagonist agents under development at both the pre-clinical and clinical stages.
  • Some major classes of TGF- ⁇ inhibitors include ligand traps, antisense oligonucleotides (ASO), small molecule receptor kinase inhibitors, and peptide aptamers.
  • Ligand traps serve as a sink for the excess TGF- ⁇ produced by tumor cells and fibroblasts during cancer progression, which increases with aggressiveness and tumor stage.
  • Ligand traps can also include anti-ligand neutralizing antibodies and soluble decoy receptor proteins that incorporate the ectodomains from either T ⁇ KII or ⁇ RIII/betaglycan protein.
  • Neutralizing antibodies have been raised against individual ligands or may be designed to block all three isomers.
  • One example includes a pan-neutralizing anti-mouse TGF- ⁇ monoclonal antibody, 1D11.
  • decoy receptor proteins include recombinant Fc-fusion proteins containing the soluble ectodomain of either T ⁇ RII (T ⁇ RII-Fc) or the type III receptor, betaglycan.
  • ASOs can also be used to reduce the bioavailability of active TGF- ⁇ ligands in the local tumor microenvironment by blocking TGF- ⁇ synthesis.
  • ASOs are single-stranded polynucleotide molecules, 13-25 nucleotide in length, that hybridize to complementary RNA, inhibiting mRNA function, and preventing protein synthesis through accelerated mRNA degradation by RNase H.
  • One example includes AP12009 (Trabedersen).
  • Another therapeutic strategy is to block T ⁇ RI activity through the use of small molecule receptor kinase inhibitors that act via ATP-competitive inhibition of the kinase catalytic activity of the receptor.
  • small molecule inhibitor of T ⁇ RI SB-431542, T ⁇ RI/ALK5 kinase inhibitor, Ki26894, T ⁇ RI inhibitor SD-208, dual inhibitor of T ⁇ RI/II, LY2109761, or inhibitors selective for the kinase domain of the type 1 TGF- ⁇ receptor, LY2157299.
  • EGFR erlotinib
  • ABL/PDGFR/KIT imatinib
  • VEGFR/RAF/PDGFR sorafenib
  • peptide aptamers are small peptide molecules containing a target-binding and a scaffolding domain that stabilize and interfere with the function of the target. Aptamers may therefore be designed specifically against Smad2 versus Smad3, and against multimeric transcriptional complexes containing Smads and other transcription factors, transcriptional co-activators, or co-repressors.
  • the Trx-SARA aptamer is and has been reported to reduce the levels of Smad2/3

Claims (6)

  1. Procédé de détermination d'un pronostic de cancer chez un individu, comprenant :
    la détermination de la présence ou de l'absence d'un taux élevé d'expression dans un échantillon de l'individu par rapport à un standard de référence normal pour un panel pronostique comprenant les marqueurs suivants :
    AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3 et VCAN, et
    le pronostic d'un cas de cancer s'il est démontré chez l'individu la présence d'un taux élevé d'expression par rapport à un standard de référence normal, le cancer étant un cancer de l'ovaire avec une survie globale faible.
  2. Procédé selon la revendication 1, dans lequel le pronostic permet une sélection thérapeutique pour l'individu pronostiqué, choisi dans le groupe constitué de : une chimiothérapie, une radiothérapie, une chirurgie, et des combinaisons de celles-ci.
  3. Procédé de détermination d'un diagnostic de cancer chez un individu suspecté d'avoir un cancer, comprenant :
    la détermination de la présence ou de l'absence d'un taux élevé d'expression dans un échantillon de l'individu par rapport à un standard de référence normal pour un panel diagnostique comprenant les marqueurs suivants :
    AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3 et VCAN, et
    le diagnostic d'un cas de cancer s'il est démontré chez l'individu la présence d'un taux élevé d'expression par rapport à un standard de référence normal, le cancer étant un cancer de l'ovaire avec une survie globale faible.
  4. Procédé selon la revendication 3, dans lequel le diagnostic permet une classification de sous-type moléculaire pour le cas de cancer diagnostiqué chez l'individu.
  5. Procédé de détermination du sous-type d'un cancer gynécologique, comprenant :
    la détermination de la présence ou de l'absence d'un taux élevé d'expression chez l'individu par rapport à un standard de référence normal pour un panel pronostique comprenant les marqueurs suivants :
    AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3 et VCAN, et
    la détermination du sous-type du cancer gynécologique d'un cas de cancer s'il est démontré chez l'individu la présence d'un taux élevé d'expression par rapport à un standard de référence normal, le cancer gynécologique étant un cancer de l'ovaire et le sous-type étant un cancer de l'ovaire séreux.
  6. Procédé selon la revendication 5,
    (i) dans lequel le cancer de l'ovaire est caractérisé par une activité élevée de transition stromale ou épithélio-mésenchymateuse ou
    (ii) dans lequel la détermination du sous-type du cancer gynécologique indique un traitement thérapeutique ou
    (iii) dans lequel le traitement thérapeutique est une immunothérapie ou est antifibrotique ou module l'activité de la voie TGF-β.
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US10253368B2 (en) 2019-04-09
EP2908913A4 (fr) 2016-10-12
AU2013331154B2 (en) 2018-02-01
AU2013331154A1 (en) 2015-04-16
US20150322530A1 (en) 2015-11-12
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